Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang+5 more
wiley +1 more source
Heuristic pruning of decision trees at low probabilities and probability discounting in sequential planning in young and older adults. [PDF]
Sass SH+7 more
europepmc +1 more source
Examining influencing factors of express delivery stations' spatial distribution using the gradient boosting decision trees: A case study of Nanjing, China. [PDF]
He Q, Sun S.
europepmc +1 more source
Liquid Metal Sensors for Soft Robots
This review thoroughly reviews liquid metal sensors in soft robots. Their unique material properties like high conductivity and good biocompatibility are analyzed. Working principles are classified, and applications in environmental perception, motion detection, and human—robot interaction are introduced.
Qi Zhang+7 more
wiley +1 more source
Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees. [PDF]
Aswal S, Ahuja NJ, Mehra R.
europepmc +1 more source
Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization. [PDF]
Cui Y.
europepmc +1 more source
Using the Dual of Proximity Graphs for Binary Decision Tree Design [PDF]
J. Salvador Sánchez+2 more
openalex +1 more source
This review systematically examines robotic systems for robot‐assisted transoral surgical procedures, classifying them based on transoral access depth, and evaluates their fundamental design principles, mechanical innovations, algorithmic advancements, and clinical implementation status.
Yuhao Shi+5 more
wiley +1 more source
MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data. [PDF]
Settelmeier J+11 more
europepmc +1 more source